CV
I am a PhD student in Information Engineering at the University of Padova, with a background in Physics of Data and experience across machine learning research, applied AI systems, and scientific computing.
My work combines neuromorphic computing, spiking neural networks, adaptive learning, and research software, with additional experience in multimodal machine learning, quantitative modeling, and data-driven system design.
Education
PhD in Information Engineering
University of Padova
Research on neuromorphic computing, spiking neural networks, and biologically inspired learning algorithms.
MS in Physics of Data
University of Padova
Graduated with 110/110 cum laude. Thesis on deep spiking neural network architectures for reward-modulated STDP learning.
BS in Physics
University of Padova
Foundation in physics, mathematics, scientific computing, and data analysis.
Experience
Teaching Assistant
University of Padova
Supported teaching activities, practical exercises, and student guidance in technical subjects, with emphasis on clarity, problem-solving, and structured explanations.
Machine Learning Engineer
Competitoor – Deda Stealth
Worked on multimodal classification pipelines combining vision and language models for e-commerce applications, with a focus on model development, evaluation, and production-oriented workflows.
Research Intern
University of Padova
Conducted research on spiking neural networks, including model design, implementation, and experimental work on biologically inspired learning and object recognition.
Quant Analyst
XSOR Capital
Developed quantitative and machine learning models, working on predictive modeling, backtesting workflows, and data-driven analysis.
Private Tutor
Ferrara, Italy
Teaching mathematics, physics, and computer science in one-on-one settings, with emphasis on clarity, adaptability, and strong conceptual understanding.